RT info:eu-repo/semantics/article
T1 Symbolic dynamics to enhance diagnostic ability of portable oximetry from the Phone Oximeter in the detection of paediatric sleep apnoea
A1 Álvarez González, Daniel
A1 Crespo, Andrea
A1 Vaquerizo Villar, Fernando
A1 Gutierrez Tobal, Gonzalo César
A1 Cerezo Hernández, Ana
A1 Barroso García, Verónica
A1 Ansermino, J. Mark
A1 Dumont, Guy A
A1 Hornero Sánchez, Roberto
A1 Campo Matias, Félix del
A1 Garde, Ainara
AB Objective: This study is aimed at assessing symbolic dynamics as a reliable technique to characterise complex fluctuations of portable oximetry in the context of automated detection of childhood obstructive sleep apnoea-hypopnoea syndrome (OSAHS). Approach: Nocturnal oximetry signals from 142 children with suspected OSAHS were acquired using the Phone Oximeter: a portable device that integrates a pulse oximeter with a smartphone. An apnoea-hypopnoea index (AHI) ⩾ 5 events h−1 from simultaneous in-lab polysomnography was used to confirm moderate-to-severe childhood OSAHS. Symbolic dynamics was used to parameterise non-linear changes in the overnight oximetry profile. Conventional indices, anthropometric measures, and time-domain linear statistics were also considered. Forward stepwise logistic regression was used to obtain an optimum feature subset. Logistic regression (LR) was used to identify children with moderate-to-severe OSAHS. Main results: The histogram of 3-symbol words from symbolic dynamics showed significant differences (p < 0.01) between children with AHI < 5 events h−1 and moderate-to-severe patients (AHI ⩾ 5 events h−1). Words representing increasing oximetry values after apnoeic events (re-saturations) showed relevant diagnostic information. Regarding the performance of individual characterization approaches, the LR model composed of features from symbolic dynamics alone reached a maximum performance of 78.4% accuracy (65.2% sensitivity; 86.8% specificity) and 0.83 area under the ROC curve (AUC). The classification performance improved combining all features. The optimum model from feature selection achieved 83.3% accuracy (73.5% sensitivity; 89.5% specificity) and 0.89 AUC, significantly (p <0.01) outperforming the other models. Significance: Symbolic dynamics provides complementary information to conventional oximetry analysis enabling reliable detection of moderate-to-severe paediatric OSAHS from portable oximetry.
PB IOP Publishing
SN 0967-3334
YR 2018
FD 2018
LK https://uvadoc.uva.es/handle/10324/74132
UL https://uvadoc.uva.es/handle/10324/74132
LA eng
NO Physiological Measurement, 2018, vol. 39, p. 104002 (16pp)
NO Producción Científica
DS UVaDOC
RD 05-abr-2025